Comparative Analysis of Bacteriophytochrome Agp2 and Its Engineered Photoactivatable NIR Fluorescent Proteins PAiRFP1 and PAiRFP2 by Faez Iqbal Khan, Fakhrul Hassan, Razique Anwer, Feng Juan, Dakun Lai

“…The eigenvalues and the trace of covariance matrix were found to be high for PAiRFP1 (597.90 nm²) and PAiRFP2 (726.74 nm²) when compared with Agp2 (535.79 nm²). …”
Get full text

Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization by Linghui Xu, Linghui Xu, Xiaoguang Liu, Yuting Chen, Linfan Yu, Linfan Yu, Zehao Yan, Zehao Yan, Canjun Yang, Canjun Yang, Congcong Zhou, Wei Yang, Wei Yang

“…Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. …”
Get full text

Extending the satellite data record of tropospheric ozone profiles from Aura-TES to MetOp-IASI: characterisation of optimal estimation retrievals by H. Oetjen, V. H. Payne, S. S. Kulawik, A. Eldering, J. Worden, D. P. Edwards, G. L. Francis, H. M. Worden, C. Clerbaux, J. Hadji-Lazaro, D. Hurtmans

“…In addition to evaluating biases, we validate the retrieval errors by comparing predicted errors to the sample covariance matrix of the IASI observations themselves. …”
Get full text

Method for control by orbital spacecraft magnetic cleanliness based on multiple magnetic dipole models with consideration of their uncertainty by B. I. Kuznetsov, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets, B. B. Kobylianskyi

“…Weight matrix calculated as inverse covariance matrix of random errors vector. Values of magnetic moments and coordinates of placement of compensating magnetic dipoles for compensation of the orbital spacecraft initial magnetic field also calculated as solution of nonlinear minimax optimization problem. …”
Get full text

Harmonisation and diagnostics of MIPAS ESA CH₄ and N₂O profiles using data assimilation by Q. Errera, S. Ceccherini, Y. Christophe, S. Chabrillat, M. I. Hegglin, A. Lambert, R. Ménard, P. Raspollini, S. Skachko, M. van Weele, K. A. Walker

“…Using the averaging kernels of the observations and a background error covariance matrix, which has previously been calibrated, allows the system to partly remedy this issue and provide assimilated fields that are more regular vertically. …”
Get full text

Uncertainty Quantification of PWR Fuel Assembly Performance Analysis Based on Statistical Sampling Method by ZOU Xiaoyang;CAO Liangzhi;LIU Zhouyu;WU Hongchun

“…Then, according to the characteristics of sparse and unfilled rank covariance matrix of nuclear data, COST method was applied to reduce the sample size. …”
Get full text

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search by Yujian Jiang, Saisai Yu, Tianhao Wang, Zhaoneng Sun, Shuang Wang

“…Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. …”
Get full text

A SAR-GMTI Approach Aided by Online Knowledge With an Airborne Multichannel Quad-Pol Radar System by Chaolei Han, Zhiwei Yang, Guisheng Liao, Qingjun Zhang, Shun He, Huajian Xu

“…In the complicated geographical environment, there will be a seriously deleterious effect to the performance of synthetic aperture radar (SAR)-ground moving target indication (SAR-GMTI) system, because it is difficult to obtain the homogeneous training samples to accurately estimate the clutter covariance matrix (CCM) without prior information of the observed scene. …”
Get full text

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity by Payam Amini, Payam Amini, Morteza Hajihosseini, Morteza Hajihosseini, Saumyadipta Pyne, Saumyadipta Pyne, Irina Dinu

“…At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. …”
Get full text

Estimates of genetic parameters for fat yield in Murrah buffaloes by Manoj Kumar, Vikas Vohra, Poonam Ratwan, Jamuna Valsalan, C. S. Patil, A. K. Chakravarty

“…Genetic and phenotypic correlations among MTDFY, and 305-day fat yield were calculated from the analysis of variance and covariance matrix among sire groups. Results: The overall least squares mean of L305FY was found to be 175.74±4.12 kg. …”
Get full text

Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice by Stefan Kern

“…However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. …”
Get full text

Implicit Equal-Weights Variational Particle Smoother by Pinqiang Wang, Mengbin Zhu, Yan Chen, Weimin Zhang

“…To overcome the difficulty of the implicit equal-weights particle filter in real geophysical application, the posterior error covariance matrix is estimated using a limited ensemble and can be calculated in parallel. …”
Get full text

MSGL+: Fast and Reliable Model Selection-Inspired Graph Metric Learning by Cheng Yang, Fei Zheng, Yujie Zou, Liang Xue, Chao Jiang, Shuangyu Liu, Bochao Zhao, Haoyang Cui

“…MSGL+ is inspired from model selection, leverages recent advancements in graph spectral signal processing (GSP), and offers several key innovations: (1) Polynomial Interpretation: We use a polynomial function of a certain order on the graph Laplacian to represent the inverse covariance matrix of the graph nodes to rigorously formulate an optimization problem. (2) Convex Formulation: We formulate a convex optimization objective with a cone constraint that optimizes the coefficients of the polynomial, which makes our approach efficient. (3) Linear Constraints: We convert the cone constraint of the objective to a set of linear ones to further ensure the efficiency of our method. (4) Optimization Objective: We explore the properties of these linear constraints within the optimization objective, avoiding sub-optimal results by the removal of the box constraints on the optimization variables, and successfully further reduce the number of variables compared to our preliminary work, MSGL. (5) Efficient Solution: We solve the objective using the efficient linear-program-based Frank–Wolfe algorithm. …”
Get full text

A P300-Based Speller Design Using a MINMAX Riemannian Geometry Scheme and Convolutional Neural Network by Seyedeh Nadia Aghili, Abbas Erfanian

“…However, for large data sets, when the number of channels is large, we encounter the curse of dimensionality problem in computing the covariance matrix and constructing the Riemannian graph. …”
Get full text

Pilot study for generating and assessing nomograms and decision curves analysis to predict clinically significant prostate cancer using only spatially registered multi-parametric M... by Rulon Mayer, Rulon Mayer, Baris Turkbey, Peter Choyke, Charles B. Simone

“…Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor’s eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). …”
Get full text

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA? by Jonathan C. Moyer, Patrick J. Heagerty, David M. Murray

“…However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. …”
Get full text

Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters by Yusuke Toda, Akito Kaga, Hiromi Kajiya‐Kanegae, Tomohiro Hattori, Shuhei Yamaoka, Masanori Okamoto, Hisashi Tsujimoto, Hiroyoshi Iwata

“…However, the present multitrait GP model cannot incorporate high‐dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. …”
Get full text

Repetibilidade de caracteres de fruto em araçazeiro e pitangueira Repeatability traits of strawberry guava and surinam cherry fruits by Moeses Andrigo Danner, Maria do Carmo Bassols Raseira, Simone Aparecida Zolet Sasso, Idemir Citadin, Silvia Scariot

“…The repeatability coefficient was better estimated by the method of principal components analysis, based on covariance matrix. The fruit weight is adequate to phenotypic selection in strawberry guava and surinam cherry. …”
Get full text

Opposition-Based Chaotic Tunicate Swarm Algorithms for Global Optimization by Tapas Si, Pericles B. C. Miranda, Utpal Nandi, Nanda Dulal Jana, Saurav Mallik, Ujjwal Maulik, Hong Qin

“…The experimental results are compared with the classical TSA, TSA with the local escaping operator (TSA-LEO), Sine Cosine Algorithm (SCA), Giza-Pyramid Construction Algorithm (GPC), Covariance Matrix Adaptation Evolution Strategy (CMAES), Archimedes Optimization Algorithm (AOA), Opposition-Based Arithmetic Optimization Algorithm (OBLAOA), and Opposition-Based Chimp Optimization Algorithm (ChOAOBL). …”
Get full text

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold by Guiying Xu, Zhenyu Wang, Xi Zhao, Ruxue Li, Ting Zhou, Tianheng Xu, Honglin Hu

“…Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. …”
Get full text